Sleep deficiency in spaceflight is associated with degraded neurobehavioral functions and elevated stress in astronauts on six-month missions aboard the International Space Station

Astronauts are required to maintain optimal neurobehavioral functioning despite chronic exposure to the stressors and challenges of spaceflight. Sleep of adequate quality and duration is fundamental to neurobehavioral functioning, however astronauts commonly experience short sleep durations in spaceflight (<6 h). As humans embark on long-duration space exploration missions, there is an outstanding need to identify the consequences of sleep deficiency in spaceflight on neurobehavioral functions. Therefore, we conducted a longitudinal study that examined the sleep-wake behaviors, neurobehavioral functions, and ratings of stress and workload of N=24 astronauts before, during, and after 6-month missions aboard the International Space Station (ISS). The computerized, Reaction SelfTest (RST), gathered astronaut report of sleep-wake behaviors, stress, workload, and somatic behavioral states; the RST also objectively assessed vigilant attention (i.e., Psychomotor Vigilance Test-Brief). Data collection began 180 days before launch, continued every 4 days in-flight aboard the ISS, and up to 90 days post-landing, which produced N=2,856 RSTs. Consistent with previous ISS studies, astronauts reported sleeping ~6.5 h in-flight. The adverse consequences of short sleep were observed across neurobehavioral functions, where sleep durations <6 h were associated with significant reductions in psychomotor response speed, elevated stress, and higher workload. Sleep durations <5 h were associated with elevated negative somatic behavioral states. Furthermore, longer sleep durations had beneficial effects on astronaut neurobehavioral functions. Taken together, our findings highlight the importance of sleep for the maintenance of neurobehavioral functioning and as with humans on Earth, astronauts would likely benefit from interventions that promote sleep duration and quality.

Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal.

Applied Mechatronics: On Mitigating Disturbance Effects in MEMS Resonators Using Robust Nonsingular Terminal Sliding Mode Controllers

This investigation attempts to study a possible controller in improving the dynamic stability of capacitive microstructures through mitigating the effects of disturbances and uncertainties in their resultant dynamic behavior. Consequently, a nonsingular terminal sliding mode control strategy is suggested in this regard. The main features of this particular control strategy are its high response speed and its non-reliance on powerful controller forces. The stability of the controller was investigated using Lyapunov theory. For this purpose, a suitable Lyapunov function was introduced to prove the stability of a controller, and the singularity conditions and methods to overcome these conditions are presented. The achieved results proved the high capability of the applied technique in stabilizing of the microstructure as well as mitigating the effects of disturbances and uncertainties.

Modeling and analysis of responsive space launch system based on timed colored Petri NeT

Responsive space launch is currently developing rapidly and has application cases in many fields, with advantages such as fast response speed and flexible networking. Aiming at the efficiency evaluation of responsive space launch system, this article first summarizes the typical process of fast space launch, and then gives a hierarchical model of a space fast launch system based on timed colored Petri nets, including top-level model, demand model, command decision model, launch Model, measurement and control model, and finally use CPN Tools to simulate and verify the model, analyze the basic characteristics of the model and the performance of the system, and provide data support and theoretical basis for the optimization of the fast space launch system.

Smart City Rail Automatic Fare Collection System Under the Background of Big Data

With the rapid development of Internet technology, the process of urban construction is accelerating and the level of urbanization is further improved. In the smart city rail automatic fare collection system, there are a large number of data information and data that need to be processed manually. The traditional manual method not only consumes human, material and financial resources, but also has low efficiency. Therefore, this paper proposes a smart city rail automatic fare collection system based on big data design. Firstly, this paper expounds the concept of smart city rail transit and studies the function of automatic fare collection system. Then it studies the definition and characteristics of big data, designs the method of system development, and tests the performance of the system. The test results show that the system runs smoothly, accounts for a relatively small amount of memory, has a fast response speed and low delay. Most passengers are satisfied with the system.

Tin oxide/Silicon Heterojunction with Nano Litchi Shell Structure for Ultrafast, High-Detectivity, Self-Powered Broadband Photodetectors

Self-powered photodetectors with excellent figure-of-merits, fast response speed, and broadband detection capability have drawn tremendous research interest. However, it is still challenging to develop excellent light-absorbing materials as photodetectors with...

A Novel Performance Adaptation and Diagnostic Method for Aero-Engines Based on the Aerothermodynamic Inverse Model

Aero-engines are faced with severe challenges of availability and reliability in the increasing operation, and traditional gas path filtering diagnostic methods have limitations restricted by various factors such as strong nonlinearity of the system and lack of critical sensor information. A method based on the aerothermodynamic inverse model (AIM) is proposed to improve the adaptation accuracy and fault diagnostic dynamic estimation response speed in this paper. Thermodynamic mechanisms are utilized to develop AIM, and scaling factors are designed to be calculated iteratively in the presence of measurement correction. In addition, the proposed method is implemented in combination with compensation of the nonlinear filter for real-time estimation of health parameters under the hypothesis of estimated dimensionality reduction. Simulations involved experimental datasets revealed that the maximum average simulated error decreased from 13.73% to 0.46% through adaptation. It was also shown that the dynamic estimated convergence time of the improved diagnostic method reached 2.183 s decrease averagely without divergence compared to the traditional diagnostic method. This paper demonstrates the proposed method has the capacity to generalize aero-engine adaptation approaches and to achieve unbiased estimation with fast convergence in performance diagnostic techniques.

“What Is Hidden behind the Mask?” Facial Emotion Recognition at the Time of COVID-19 Pandemic in Cognitively Normal Multiple Sclerosis Patients

Social cognition deficits have been described in people with multiple sclerosis (PwMS), even in absence of a global cognitive impairment, affecting predominantly the ability to adequately process emotions from human faces. The COVID-19 pandemic has forced people to wear face masks that might interfere with facial emotion recognition. Therefore, in the present study, we aimed at investigating the ability of emotion recognition in PwMS from faces wearing masks. We enrolled a total of 42 cognitively normal relapsing–remitting PwMS and a matched group of 20 healthy controls (HCs). Participants underwent a facial emotion recognition task in which they had to recognize from faces wearing or not surgical masks which of the six basic emotions (happiness, anger, fear, sadness, surprise, disgust) was presented. Results showed that face masks negatively affected emotion recognition in all participants (p < 0.001); in particular, PwMS showed a global worse accuracy than HCs (p = 0.005), mainly driven by the “no masked” (p = 0.021) than the “masked” (p = 0.064) condition. Considering individual emotions, PwMS showed a selective impairment in the recognition of fear, compared with HCs, in both the conditions investigated (“masked”: p = 0.023; “no masked”: p = 0.016). Face masks affected negatively also response times (p < 0.001); in particular, PwMS were globally hastier than HCs (p = 0.024), especially in the “masked” condition (p = 0.013). Furthermore, a detailed characterization of the performance of PwMS and HCs in terms of accuracy and response speed was proposed. Results from the present study showed the effect of face masks on the ability to process facial emotions in PwMS, compared with HCs. Healthcare professionals working with PwMS at the time of the COVID-19 outbreak should take into consideration this effect in their clinical practice. Implications in the everyday life of PwMS are also discussed.

Research on automobile four-wheel steering control system based on yaw angular velocity and centroid cornering angle

In order to improve the handling stability of four-wheel steering (4WS) cars, a two-degree-of-freedom 4WS vehicle dynamics model is constructed here, and the motion differential equation of the system model is established. Based on the quadratic optimal control theory, the optimal control of 4WS system is proposed in this paper. When running at low speed and high speed, through yaw rate feedback control, state feedback control, and optimal control, the 4WS cars are controlled based on yaw rate and centroid cornering angle with MATLAB/Simulink simulation. The result indicates that 4WS control based on the optimal control can improve the displacement of the cars. And, the optimal control of 4WS proposed in this paper can eliminate centroid cornering angle completely compared with other two traditional optimal control methods. Besides, the optimal control enjoys faster response speed and no overshoot happens. In conclusion, the optimal control method proposed in the paper represents better stability, moving track and stability, thereby further enhancing the handling property of cars.